OpenRaiser/Intern-Atlas huggingface dataset:
A Semantic Model for Interacting Cyber-Physical Systems
Venue: arXiv cs.SE 2021
Year: 2021

We propose a component-based semantic model for Cyber-Physical Systems (CPSs) wherein the notion of a component abstracts the internal details of both cyber and physical processes, to expose a uniform semantic model of their externally observable behaviors expressed as sets of sequences of observations. We introduce algebraic operations on such sequences to model different kinds of component composition. These composition operators yield the externally observable behavior of their resulting composite components through specifications of interactions of the behaviors of their constituent components, as they, e.g., synchronize with or mutually exclude each other's alternative behaviors. Our framework is expressive enough to allow articulation of properties that coordinate desired interactions among composed components within the framework, also as component behavior. We demonstrate the usefulness of our formalism through examples of coordination properties in a CPS consisting of two robots interacting through shared physical resources.
Communication-Efficient Federated Learning with Binary Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of the parameters between all the clients and the server that coordinates the training. This introduces extensive communication overhead, which can be a major bottleneck in FL with limited communication links. In this paper, we consider training the binary neural networks (BNN) in the FL setting instead of the typical real-valued neural networks to fulfill the stringent delay and efficiency requirement in wireless edge networks. We introduce a novel FL framework of training BNN, where the clients only upload the binary parameters to the server. We also propose a novel parameter updating scheme based on the Maximum Likelihood (ML) estimation that preserves the performance of the BNN even without the availability of aggregated real-valued auxiliary parameters that are usually needed during the training of the BNN. Moreover, for the first time in the literature, we theoretically derive the conditions under which the training of BNN is converging. { Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.
Coarsening Optimization for Differentiable Programming
Venue: arXiv cs.PL 2021
Year: 2021

This paper presents a novel optimization for differentiable programming named coarsening optimization. It offers a systematic way to synergize symbolic differentiation and algorithmic differentiation (AD). Through it, the granularity of the computations differentiated by each step in AD can become much larger than a single operation, and hence lead to much reduced runtime computations and data allocations in AD. To circumvent the difficulties that control flow creates to symbolic differentiation in coarsening, this work introduces phi-calculus, a novel method to allow symbolic reasoning and differentiation of computations that involve branches and loops. It further avoids "expression swell" in symbolic differentiation and balance reuse and coarsening through the design of reuse-centric segment of interest identification. Experiments on a collection of real-world applications show that coarsening optimization is effective in speeding up AD, producing several times to two orders of magnitude speedups.
COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India
Venue: arXiv cs.CL 2021
Year: 2021

While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale. Much of the data exists in unstructured form on the web, and limited aspects of such data are available through public APIs maintained manually through volunteer effort. This has proved to be difficult both in terms of ease of access to detailed data and with regards to the maintenance of manual data-keeping over time. This paper reports on our effort at automating the extraction of such data from public health bulletins with the help of a combination of classical PDF parsers and state-of-the-art machine learning techniques. In this paper, we will describe the automated data-extraction technique, the nature of the generated data, and exciting avenues of ongoing work.
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources
Venue: arXiv cs.LG 2021
Year: 2021

Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures. These alerts often need to be manually investigated by specialists. Given the operational cost of manual inspections, the suspicious events are selected by alerting systems with carefully designed thresholds. In this paper, we consider an imbalanced binary classification problem, where events arrive sequentially and only a limited number of suspicious events can be inspected. We model the event arrivals as a non-homogeneous Poisson process, and compare various suspicious event selection methods including those based on static and adaptive thresholds. For each method, we analytically characterize the tradeoff between the minority-class detection rate and the inspection capacity as a function of the data class imbalance and the classifier confidence score densities. We implement the selection methods on a real public fraud detection dataset and compare the empirical results with analytical bounds. Finally, we investigate how class imbalance and the choice of classifier impact the tradeoff.
A Deep Learning-based Audio-in-Image Watermarking Scheme
Venue: arXiv cs.MM 2021
Year: 2021

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.
Reward-Punishment Symmetric Universal Intelligence
Venue: arXiv cs.AI 2021
Year: 2021

Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We show that if the background encodings and Universal Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the resulting Legg-Hutter intelligence measure is symmetric about the origin. In particular, this implies reward-ignoring agents have Legg-Hutter intelligence 0 according to such UTMs.
A logical approach for temporal and multiplex networks analysis
Venue: arXiv cs.SI 2021
Year: 2021

Many systems generate data as a set of triplets (a, b, c): they may represent that user a called b at time c or that customer a purchased product b in store c. These datasets are traditionally studied as networks with an extra dimension (time or layer), for which the fields of temporal and multiplex networks have extended graph theory to account for the new dimension. However, such frameworks detach one variable from the others and allow to extend one same concept in many ways, making it hard to capture patterns across all dimensions and to identify the best definitions for a given dataset. This extended abstract overrides this vision and proposes a direct processing of the set of triplets. In particular, our work shows that a more general analysis is possible by partitioning the data and building categorical propositions that encode informative patterns. We show that several concepts from graph theory can be framed under this formalism and leverage such insights to extend the concepts to data triplets. Lastly, we propose an algorithm to list propositions satisfying specific constraints and apply it to a real world dataset.
T-SNE Is Not Optimized to Reveal Clusters in Data
Venue: arXiv cs.LG 2021
Year: 2021

Cluster visualization is an essential task for nonlinear dimensionality reduction as a data analysis tool. It is often believed that Student t-Distributed Stochastic Neighbor Embedding (t-SNE) can show clusters for well clusterable data, with a smaller Kullback-Leibler divergence corresponding to a better quality. There was even theoretical proof for the guarantee of this property. However, we point out that this is not necessarily the case -- t-SNE may leave clustering patterns hidden despite strong signals present in the data. Extensive empirical evidence is provided to support our claim. First, several real-world counter-examples are presented, where t-SNE fails even if the input neighborhoods are well clusterable. Tuning hyperparameters in t-SNE or using better optimization algorithms does not help solve this issue because a better t-SNE learning objective can correspond to a worse cluster embedding. Second, we check the assumptions in the clustering guarantee of t-SNE and find they are often violated for real-world data sets.
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
Venue: arXiv cs.CV 2021
Year: 2021

Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. Moreover, the diverse disciplines of science, including remote sensing, have utilised tremendous improvements in image classification by Convolutional Neural Networks (CNNs) with transfer learning. In this study, instead of training CNNs from scratch, the transfer learning is applied to fine-tune pre-trained networks Visual Geometry Group (VGG16) and Wide Residual Networks (WRNs), by replacing the final layer with additional layers, for LULC classification using the red-green-blue version of the EuroSAT dataset. Moreover, the performance and computational time are compared and optimised with techniques, such as early stopping, gradient clipping, adaptive learning rates, and data augmentation. The proposed approaches have addressed the limited-data problem, and very good accuracies are achieved. The results show that the proposed method based on the WRNs performs better than the previous best-stated results in terms of the computational efficiency and accuracy from 98.57% to 99.17%.
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks
Venue: arXiv cs.CV 2021
Year: 2021

Deep neural networks (DNNs) have achieved great success in the area of computer vision. The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods. However, the existing DNNs hardly serve both efficient computation and rich expression capability, which makes them difficult for deployment in real-time and high-quality applications, especially on mobile devices. To this end, we propose an efficient, accurate, and configurable deep network for disparity estimation named FADNet++. Leveraging several liberal network design and training techniques, FADNet++ can boost its accuracy with a fast model inference speed for real-time applications. Besides, it enables users to easily configure different sizes of models for balancing accuracy and inference efficiency. We conduct extensive experiments to demonstrate the effectiveness of FADNet++ on both synthetic and realistic datasets among six GPU devices varying from server to mobile platforms. Experimental results show that FADNet++ and its variants achieve state-of-the-art prediction accuracy, and run at a significant order of magnitude faster speed than existing 3D models. With the constraint of running at above 15 frames per second (FPS) on a mobile GPU, FADNet++ achieves a new state-of-the-art result for the SceneFlow dataset.
InterpolationSLAM: A Novel Robust Visual SLAM System in Rotating Scenes
Venue: arXiv cs.RO 2021
Year: 2021

In recent years, visual SLAM has achieved great progress and development, but in complex scenes, especially rotating scenes, the error of mapping will increase significantly, and the slam system is easy to lose track. In this article, we propose an InterpolationSLAM framework, which is a visual SLAM framework based on ORB-SLAM2. InterpolationSLAM is robust in rotating scenes for Monocular and RGB-D configurations. By detecting the rotation and performing interpolation processing at the rotated position, pose of the system can be estimated more accurately at the rotated position, thereby improving the accuracy and robustness of the SLAM system in the rotating scenes. To the best of our knowledge, it is the first work combining the interpolation network into a Visual SLAM system to improve SLAM system robustness in rotating scenes. We conduct experiments both on KITTI Monocular and TUM RGB-D datasets. The results demonstrate that InterpolationSLAM outperforms the accuracy of standard Visual SLAM baselines.
Bach Style Music Authoring System based on Deep Learning
Venue: arXiv cs.AI 2021
Year: 2021

With the continuous improvement in various aspects in the field of artificial intelligence, the momentum of artificial intelligence with deep learning capabilities into the field of music is coming. The research purpose of this paper is to design a Bach style music authoring system based on deep learning. We use a LSTM neural network to train serialized and standardized music feature data. By repeated experiments, we find the optimal LSTM model which can generate imitation of Bach music. Finally the generated music is comprehensively evaluated in the form of online audition and Turing test. The repertoires which the music generation system constructed in this article are very close to the style of Bach's original music, and it is relatively difficult for ordinary people to distinguish the musics Bach authored and AI created.
Digital Divide and Social Dilemma of Privacy Preservation
Venue: arXiv cs.CY 2021
Year: 2021

While digital divide studies primarily focused on access to information and communications technology (ICT) in the past, its influence on other associated dimensions such as privacy is becoming critical with a far-reaching impact on the people and society. For example, the various levels of government legislation and compliance on information privacy worldwide have created a new era of digital divide in the privacy preservation domain. In this article, the concept "digital privacy divide (DPD)" is introduced to describe the perceived gap in the privacy preservation of individuals based on the geopolitical location of different countries. To better understand the DPD phenomenon, we created an online questionnaire and collected answers from more than 700 respondents from four different countries (the United States, Germany, Bangladesh, and India) who come from two distinct cultural orientations as per Hofstede's individualist vs. collectivist society. However, our results revealed some interesting findings. DPD does not depend on Hofstede's cultural orientation of the countries. For example, individuals residing in Germany and Bangladesh share similar privacy concerns, while there is a significant similarity among individuals residing in the United States and India. Moreover, while most respondents acknowledge the importance of privacy legislation to protect their digital privacy, they do not mind their governments to allow domestic companies and organizations collecting personal data on individuals residing outside their countries, if there are economic, employment, and crime prevention benefits. These results suggest a social dilemma in the perceived privacy preservation, which could be dependent on many other contextual factors beyond government legislation and countries' cultural orientation.
Donoho Starks and Hardys Uncertainty Principles for the Shortotime Quaternion Offset Linear Canonical Transform Donoho-Stark's and Hardy's Uncertainty Principles for the Short-time Quaternion Offset Linear Canonical Transform
Venue: arXiv cs.IT 2021
Year: 2021

The quaternion offset linear canonical transform (QOLCT) which is time shifted and frequency modulated version of the quaternion linear canonical transform (QLCT) provides a more general framework of most existing signal processing tools. For the generalized QOLCT, the classical Heisenbergs and Liebs uncertainty principles have been studied recently. In this paper, we first define the shorttime quaternion offset linear canonical transform (STQOLCT) and drive its relationship with the quaternion Fourier transform (QFT). The crux of the paper lies in the generalization of several well known uncertainty principles for the STQOLCT, including Donoho Starks uncertainty principle, Hardys uncertainty principle, Beurlings uncertainty principle, and Logarithmic uncertainty principle.
Posture Recognition in the Critical Care Settings using Wearable Devices
Venue: arXiv cs.HC 2021
Year: 2021

Low physical activity levels in the intensive care units (ICU) patients have been linked to adverse clinical outcomes. Therefore, there is a need for continuous and objective measurement of physical activity in the ICU to quantify the association between physical activity and patient outcomes. This measurement would also help clinicians evaluate the efficacy of proposed rehabilitation and physical therapy regimens in improving physical activity. In this study, we examined the feasibility of posture recognition in an ICU population using data from wearable sensors.
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Nearly all models for object tracking with artificial neural networks depend on appearance features extracted from a "backbone" architecture, designed for object recognition. Indeed, significant progress on object tracking has been spurred by introducing backbones that are better able to discriminate objects by their appearance. However, extensive neurophysiology and psychophysics evidence suggests that biological visual systems track objects using both appearance and motion features. Here, we introduce $\textit{PathTracker}$, a visual challenge inspired by cognitive psychology, which tests the ability of observers to learn to track objects solely by their motion. We find that standard 3D-convolutional deep network models struggle to solve this task when clutter is introduced into the generated scenes, or when objects travel long distances. This challenge reveals that tracing the path of object motion is a blind spot of feedforward neural networks. We expect that strategies for appearance-free object tracking from biological vision can inspire solutions these failures of deep neural networks.
How BPE Affects Memorization in Transformers
Venue: arXiv cs.CL 2021
Year: 2021

Training data memorization in NLP can both be beneficial (e.g., closed-book QA) and undesirable (personal data extraction). In any case, successful model training requires a non-trivial amount of memorization to store word spellings, various linguistic idiosyncrasies and common knowledge. However, little is known about what affects the memorization behavior of NLP models, as the field tends to focus on the equally important question of generalization. In this work, we demonstrate that the size of the subword vocabulary learned by Byte-Pair Encoding (BPE) greatly affects both ability and tendency of standard Transformer models to memorize training data, even when we control for the number of learned parameters. We find that with a large subword vocabulary size, Transformer models fit random mappings more easily and are more vulnerable to membership inference attacks. Similarly, given a prompt, Transformer-based language models with large subword vocabularies reproduce the training data more often. We conjecture this effect is caused by reduction in the sequences' length that happens as the BPE vocabulary grows. Our findings can allow a more informed choice of hyper-parameters, that is better tailored for a particular use-case.
The Impact of Blocking Cars on Pathloss Within a Platoon: Measurements for 26 GHz Band
Venue: arXiv cs.NI 2021
Year: 2021

Platooning is considered to be one of the possible prospective implementations of the autonomous driving concept, where the train-of-cars moves together following the platoon leader's commands. However, the practical realization of this scheme assumes the use of reliable communications between platoon members. In this paper, the results of the measurement experiment have been presented showing the impact of the blocking cars on the signal attenuation. The tests have been carried out for the high-frequency band, i.e. for 26.555 GHz. It has been observed that on one hand side, the attenuation can reach even tens of dB for 2 or 3 blocking cars, but in some locations, the impact of a two-ray propagation mitigates the presence of obstructing vehicles.
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Venue: arXiv cs.CL 2021
Year: 2021

Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learning, via simply incorporating relation prediction into the commonly used 1vsAll objective. The new training objective contains not only terms for predicting the subject and object of a given triple, but also a term for predicting the relation type. We analyse how this new objective impacts multi-relational learning in KBC: experiments on a variety of datasets and models show that relation prediction can significantly improve entity ranking, the most widely used evaluation task for KBC, yielding a 6.1% increase in MRR and 9.9% increase in Hits@1 on FB15k-237 as well as a 3.1% increase in MRR and 3.4% in Hits@1 on Aristo-v4. Moreover, we observe that the proposed objective is especially effective on highly multi-relational datasets, i.e. datasets with a large number of predicates, and generates better representations when larger embedding sizes are used.